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IEEE International Conference on Mechatronics and Automation

作者： Chunying Li Shuxiang Guo Graduate School of Engineering Kagawa University Hayashi-cho Takamatsu Japan The Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology the Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen Guangdong China

Due to the particularity and complexity of the underwater environment, the perception ability of Autonomous Underwater Vehicles (AUVs) has become a hot issue in recent years. In this paper, the detection system was developed for bioinspired Spherical Underwater Robots (SURs) based on the pressure sensor array. Firstly, the sensor array based on the SUR model was proposed using pressure sensors, adding new dimension to near-flow detection and perception method. Then, the detection system was constructed and the hydrodynamic pressure variables are analyzed. Next, the data acquisition of the pressure sensor array was described. Furthermore, the SUR prototype was assembled carrying the pressure sensor array. Finally, a series of locomotion experiments were performed to verify its performance. The experimental results validated the effectiveness of the proposed detection system, which had a certain reference value for AUV underwater detection and state estimation.

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A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

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Geoscience Frontiers 2021年第6期12卷 313-325页

作者： Xianzhe Tang Takashi Machimura Wei Liu Jiufeng Li Haoyuan Hong Graduate School of Engineering Osaka UniversityYamadaoka 2-1SuitaOsaka 565-0871Japan School of Geography South China Normal UniversityGuangzhou 510631China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023JiangsuChina Department of Geography and Regional Research University of ViennaVienna 1010Austria

The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, *** area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.

关键词： Machine learning Natural hazards information change rate Discretization method

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2.45 GHz Frequency- Tracking Electro-Optic Imaging Using Polarization CMOS Image Sensor

2.45 GHz Frequency- Tracking Electro-Optic Imaging Using Pol...

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Asia-Pacific Conference on Microwave

作者： Ryoma Okada Maya Mizuno Hironari Takehara Makito Haruta Hiroyuki Tashiro Jun Ohta Kiyotaka Sasagawa Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan Radio Research Institute National Institute of Information and Communications Technology Koganei Tokyo Japan Department of Opto-Electronic System Engineering Graduate School of Science and Technology Nara Institute of Science and Technology / Chitose Institute of Science and Technology Ikoma Chitose Nara Hokkaido Japan Faculty of Medical Sciences Graduate School of Science and Technology Nara Institute of Science and Technology /Kyushu University Ikoma Maidashi Fukuoka Japan Institute for Research Initiatives Nara Institute of Science and Technology Ikoma Nara Japan

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

In this study, we developed an optical local oscillator signal generation system that tracks the frequency of the electric field to be observed. This system was applied to our highly sensitive electric field imaging system using the electro-optic (EO) effect. It enables electric field imaging by tracking a time-varying frequency wave source, such as frequency modulation (FM). Using this configuration, we successfully imaged at the edge of a PCB antenna with a signal modulated at 2.45 GHz, FM deviation of 100 kHz, and sweep frequency of 100 kHz.

关键词： Microwave measurement Microwave antennas Time-frequency analysis Frequency modulation Optical polarization Imaging Optical imaging Optical sensors Optical modulation Electric fields

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Fast and accurate discrimination high-speed Raman imaging combined with multi-Armed bandit algorithm in reinforcement learning 9

Fast and accurate discrimination high-speed Raman imaging co...

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High-Speed Biomedical Imaging and Spectroscopy IX 2024

作者： Tabata, Koji Kawagoe, Hiroyuki Kubo, Toshiki Taylor, James N. Mochizuki, Kentaro Clement, Jean-Emmanuel Kumamoto, Yasuaki Harada, Yoshinori Nakamura, Atsuyoshi Fujita, Katsumasa Komatsuzaki, Tamiki Research Center of Mathematics for Social Creativity Research Institute for Electronic Science Hokkaido University Kita 20 Nishi 10 Kita-ku Sapporo Hokkaido Japan Hokkaido University Kita 21 Nishi 10 Kita-ku Sapporo Hokkaido Japan Department of Applied Physics Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Pathology and Cell Regulation Graduate School of Medical Science Kyoto Prefectural University of Medicine Kajii-cho Kawaramachi-Hirokoji Kamigyo Kyoto Kyoto Japan Institute for Open and Transdisciplinary Research Initiatives Osaka University Yamadaoka Suita Osaka Japan Graduate School of Information Science and Technology Hokkaido University Kita 14 Nishi 9 Kita-ku Sapporo Hokkaido Japan Advanced Photonics and Biosensing Open Innovation Laboratory AIST-Osaka University Yamadaoka Suita Osaka Japan Graduate School of Chemical Sciences and Engineering Materials Chemistry Engineering Course Hokkaido University Kita 13 Nishi 8 Kita-ku Sapporo Hokkaido Japan The Institute of Scientific and Industrial Research Osaka University Mihogaoka Ibaraki 8-1 Osaka Japan

ISBN: (纸本)9781510669659

We propose a method that combines high-speed Raman imaging with a machine learning technique, multi-Armed bandit, to achieve rapid and accurate identification of samples under observation. First, our method dvides the field of view of the sample into small sections, and it returns either 'positive' or 'negative' based on whether the sections with high anomaly indices exceed a certain proportion. Moreover, the points to be measured are determined dynamically and automatically generating a series of optimal illumination patterns. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reinforcement learning

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Awareness of Disaster Risk and Preparedness: Insights from a Survey of Children and Adults in Thailand

Awareness of Disaster Risk and Preparedness: Insights from a...

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International Symposium on Communications and information Technologies (ISCIT)

作者： Ampan Laosunthara Natt Leelawat Takumi Ohashi Titaya Sararit Mongkonkorn Srivichai Jing Tang School of Environment and Society Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Graduate School of System Design and Management Keio University Kanagawa Japan Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo Japan Faculty of Architecture Chiangmai University Chiangmai Thailand Department of Civil Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiangrai Thailand Faculty of Engineering International School of Engineering Chulakongkorn University Bangkok Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Enhancing disaster awareness and preparedness is crucial for building resilient societies. This study comprehensively assessed the levels of disaster awareness and preparedness among children and adults in Thailand through qualitative and quantitative surveys. Children strongly emphasized the importance of disaster education in school curricula, leveraging multimedia channels for risk communication, and implementing early warning systems. In contrast, adults exhibited a greater understanding of the significance of business continuity and disaster management technologies. These findings revealed disparities in disaster awareness across age groups. The insights from this study suggest age-specific engineering system approaches to information dissemination, contributing to the development of inclusive disaster preparedness strategies that incorporate diverse stakeholder perspectives. Children highlighted the need for practical training on survival skills, accessible disaster communication through SMS alerts, and monitoring systems for timely information dissemination. Adults, on the other hand, placed greater importance on business continuity planning and recognized the usefulness of technologies like satellite imagery for disaster management. By identifying these gaps and areas for improvement, this research provides valuable guidance for tailoring interventions, fostering a culture of preparedness, and harnessing technological solutions that resonate with the needs and concerns of different age cohorts within Thai society.

关键词： Training Surveys Disasters Disaster management Satellite images Planning Business continuity Stakeholders Multimedia communication Resilience

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Two flags in a semimodular lattice generate an antimatroid

arXiv

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arXiv 2022年

作者： Hayashi, Koyo Hirai, Hiroshi Department of Information and System Engineering Chuo University Tokyo112-8551 Japan Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Tokyo113-8656 Japan

A basic property in a modular lattice is that any two flags generate a distributive sublattice. It is shown (Abels 1991, Herscovic 1998) that two flags in a semimodular lattice no longer generate such a good sublattice, whereas shortest galleries connecting them form a relatively good join-sublattice. In this note, we sharpen this investigation to establish an analogue of the two-flag generation theorem for a semimodular lattice. We consider the notion of a modular convex subset, which is a subset closed under the join and meet only for modular pairs, and show that the modular convex hull of two flags in a semimodular lattice of rank n is isomorphic to a union-closed family on [n]. This family uniquely determines an antimatroid, which coincides with the join-sublattice of shortest galleries of the two *** Codes 06C10 © 2022, CC BY.

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Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Xuming Yokoya, Naoto Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo The International Institute for Earth System Science Nanjing University Nanjing210023 China The Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan The RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a Local-to-Global Cross-modal Attention-aware Fusion (LoGoCAF) framework for HSI-X classification that jointly considers efficiency, accuracy, and generalizability. LoGoCAF adopts a pixel-to-pixel two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight multilayer perceptron (MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal fusion and classification tasks. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Attention-effective multiple instance learning on weakly stem cell colony segmentation

arXiv

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arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

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Estimating Focused Pedestrian using Smooth-Pursuits Eye Movements and Point Cloud toward Assistive system for Wheelchair

Estimating Focused Pedestrian using Smooth-Pursuits Eye Move...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuto Ito Kentaro Takemura Course of Electrical and Electronic Engineering Graduate School of Engineering Tokai University Hiratsuka Kanagawa JP School of Information Science and Technology Tokai University Hiratsuka Kanagawa JP

ISBN: (纸本)9781665442084

Intelligent electric wheelchairs have been developed for personal mobility, and eye-gaze measurement is essential for comprehending the user's attention and for assisting operations. In previous studies, the gaze vector (i.e., the visual or optical axis of the eye) was projected onto the environmental map; hence, an eye tracker was installed on the wheelchair. In addition, hardware calibration, which determines the geometric relationship between the eye tracker and other sensors, such as LiDAR, was performed beforehand. Recently, wearable eye-trackers are expected to employ a daily-use device; therefore, the cooperation between sensors is essential without geometric constraints. Accordingly, we propose a method for estimating focused pedestrians using smooth-pursuit eye movements in the real world. Pedestrians are tracked using a point cloud obtained with 3D LiDAR, and the trajectories of the focused pedestrian are recorded on an environmental map constructed with simultaneous localization and mapping. Several experiments were conducted to evaluate the computational methods for the correlation between eye movements and moving objects, and we confirmed the feasibility and the current issues through these experiments.

关键词： Point cloud compression Performance evaluation Visualization Laser radar Three-dimensional displays Simultaneous localization and mapping Tracking

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